Evaluation of Grasshopper Optimization Algorithm for Optimal Operation of Surface Water Reservoirs with Reliability Constraints

Document Type : Original Article

Authors

1 Assistant Professor in University of Mohaghegh Ardabili

2 Faculty of Civil Engineering, University of Mohaghegh Ardabili

Abstract

The use of metaheuristic optimization algorithms in the optimal management of surface water reservoirs systems has expanded considerably in recent years. The reason for this can be seen in the diversity and number of constraints of these systems, a large number of decision variables, and the multiplicity of objectives of these systems. In this research, the Grasshopper optimization algorithm as one of the new metaheuristic optimization algorithms, has been used for the management of reservoir dams. Dez Reservoir Dam has been selected as a case study for two separate simple operation and hydropower production problems (60, 120, 240 and 480 variables). In addition to the usual constraints on reservoir operation, the time reliability, volume reliability constraints as well as the provision of at least a percentage of the monthly requirement in the months with unmet demands are included in this research. The results of this study showed that in different scenarios, the Grasshopper optimization algorithm was able to improve the optimal solution by about 0.5 to 12%. However, as the number of decision variables increases, the efficiency of this algorithm is reduced compared to the genetic algorithm. Regarding the stability of the solutions, in most cases the Grasshopper optimization algorithm yielded better results. In terms of runtime, the grasshopper algorithm converged about 20 to 50 percent faster than the genetic algorithm. The results of comparing the grasshopper optimization algorithm with genetic algorithm indicate the high capability of this algorithm in solving the reservoir operation optimization problem.

Keywords

References

- اکبری فرد، س.، قادری، ک. و بختیاری.1396. بررسی عملکرد چند الگوریتم هوشمند در بهینه­سازی بهره­برداری از سیستم مخازن. نشریه آبیاری و زهکشی ایران. 11(1). 57-46.
- زینلی،م ج. ، محمدرضاپور, ا ب. ، فروغی، ف. ، .2015. ارزیابی الگوریتم‌های ازدحام ذرات، ژنتیک و سیستم مورچگان پیوسته در بهره‌برداری بهینه از مخزن سد درودزن. دانش آب و خاک, 25(3), 27-38.‎
- وزیری، ح ر.، کرمی، ح.، موسوی، س،ف.، و هادیانی، ا.، 1397. بهینه­سازی بهره­برداری از سد مخزنی با الگوریتم جستجوی کلاغ براساس یک مدل تصمیم­گیری چندمعیاره. نشریه علوم آب و خاک، 22(1). 279-290.
- Afshar, A., Sharifi F., and Jalali. M.R. 2009. Non-Dominated Archiving Multi-Colony Ant Algorithm for MultiObjective Optimization: Application to Multi-Purpose Reservoir Operation. Engineering Optimization. 41 (4): 313–325.
- Afshar, A., Emami Skardi, M.J. and Masoumi, F., 2015 a. Optimizing water supply and hydropower reservoir operation rule curves: an imperialist competitive algorithm approach. EngineeringOptimization. 47(9):1208-1225.
- Afshar, A., Masoumi, F. and Solis, S.S., 2015 b. Reliability based optimum reservoir design by hybrid ACO-LP algorithm. Water Resources Management. 29(6):2045-2058.
- Afshar, A., Massoumi, F., Afshar, A., and Mariño, M. A. 2015c. State of the art review of ant colony optimization applications in water resource management. Water Resources Management. 29(11): 3891-3904.
- Afshar, A, and Masoumi, Fariborz. 2016. Waste load reallocation in river–reservoir systems: simulation–optimization approach. Environmental Earth Sciences. 75: 53. https://doi.org/10.1007/s12665-015-4812-x.
- Afshar, A., Masoumi, F., & Sandoval Solis, S. 2018. Developing a reliability-based waste load allocation strategy for river-reservoir systems. Journal of Water Resources Planning and Management. 144(9).  DOI:10.1061/(asce)wr.1943-5452.0000973.‏
- Askarzadeh, A. 2016. A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Comput. Struct, 169: 1-12.
-Bahrami, M., Bozorg-Haddad, O., and Chu, X. 2017. Application of cat swarm optimization algorithm for optimal reservoir operation. Journal of Irrigation and Drainage Engineering. 144(1). 04017057.
- Chu, S. C., and Tsai, P. W. 2007. Computational intelligence based on the behavior of cats. Int. J. Innovative Comput. Inf. Control, 3(1), 163–173.
-Dariane, A.B. and Naeini, S.M.M., 2008. Comparison of heuristic methods applied for optimal operation of water resources. Water & Waste Water (in Persian), 19(68), pp.57-66.
-Esat, V., and Hall, M.J. 1994. Water resources system optimization using genetic algorithms. Hydroinformatics. 94:225-231.
- Eskandar, H., Sadollah, A., Bahreininejad, A. and Hamdi, M. 2012. Water cycle algorithm -A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers and Structures. 110(111): 151–166.
-Garousi-Nejad, I., Bozorg-Haddad, O., Loáiciga, H.A., and Mariño, M.A. 2016. Application of the firefly algorithm to optimal operation of reservoirs with the purpose of irrigation supply and hydropower production. Journal of Irrigation and Drainage Engineering. 142(10), 04016041.
-Holland, John H. 1975. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor
- Kangrang, A. and Lokham, C., 2013. Optimal reservoir rule curves considering conditional ant colony optimization with simulation model. Journal of applied sciences, 13(1), pp.263-267.
-Kumar D.N. and Reddy J., 2007. Multipurpose reservoir operation using particle swarm optimization. Journal of Water Resources Planning and Management. 3:192-201.
-Labadie J. 2004. Optimal Operation of Multi-Reservoir Systems: State-of-the-Art Review. Journal of Water Resourses Planning and Management. ASCE 130 (2): 93–111.
-Madadgar S., and Afshar. A. 2009. An Improved Continuous Ant Algorithm for Optimization of Water Resources Problems. Water Resources Management. 23: 2119–2139.
-Momtahen, S., and Dariane, A. B. 2007. Direct search approaches using genetic algorithms for optimization of water reservoir operating policies. Journal of Water Resources Planning and Management-ASCE. 133:202-209. ttp://dx.doi.org/10.1061/(ASCE)0733-9496(2007)133:3(202)
-Nicklow, J., P. Reed, D. Savic, T. Dessalegne, L. Harrell, A. Chan-Hilton, M. Karamouz, et al. 2010. State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management. Journal of Water Resourses Planning and Management. 136 (4): 412–432.
-Oliveria, R., and Loucks, D.P. 1997. Operating rules for multireservoir systems. Water Resource Research. 33(4):839-852.
-Ostadrahimi L., Marino M.A., and Afshar A. 2012. Multi-Reservoir Operation Rules: Multi-Swarm PSO-Based Optimization Approach. Water Resources Management. 26 (2):407–427.
-Saremi, S., Mirjalili, S., and Lewis, A. 2017. Grasshopper optimization algorithm: theory and application. Advances in Engineering Software. 105: 30-47.
-Shi Y. and Eberhart R. 1998. Parameter selection in particle swarm optimization. In: Porto VW, Saravanan N, Waagen D and Eiben AE (eds). Evolutionary Programming. 7: 611-616.
-Tennant, D.L. 1976. In stream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries. 1: 6–10
-Wurbs, R. 1993. Reservoir System Simulation and Optimization Models. Journal of Water Recourses Planning and Management. 119 (4): 455–472.
-Yeh, W. G. 1985. Reservoir Management and Operations Models: A State-of-the-Art Review. Water Resources Research. 21(12):1797–1818.
Iranian Journal of Irrigation & Drainage
Volume 14, Issue 2 - Serial Number 80
July and August 2020
Pages 579-592

Files

Share

How to cite

Statistics